This module aims to

• introduce numerical methods and associated theory for modelling of financial options;
• teach students how to implement such numerical methods on computers;
• gain experience in interpreting numerical results.

The aim of this module is to introduce students to:

• the basics of stochastic calculus by using Brownian motion as an integrator
• mathematical modelling of pricing via the Black-Scholes model.

Studying data science techniques for economic predictions is crucial in today's data-driven world, as it enhances forecasting accuracy and informs economic policy and strategy. The aim of this module is to equip students with the econometric skills necessary to interpret and leverage vast amounts of economic data to make reliable predictions. We will study the logic of predictive data analysis and the most widely used methods in contemporary economic literature.

This module aims to allow students to engage with and study issued related to the mechanics of currency markets, international finance and economics. It aims to use economic principles to examine international monetary arrangements and important historical policy episodes.

The aim of this module is

• To introduce both supervised and unsupervised methods for learning from data.
• To introduce methods of dimensionality reduction.
• To introduce the R statistical programming language for implementing methods using real data.

The aims of this module are to gain familiarity with modern qualitative theory of linear PDE's with particular emphasis on second-order equations as well as to study selected aspects of modern methods for simple nonlinear PDEs.

The aim of this module is to gain familiarity with theory and techniques of static optimisation and dynamic optimisation.